The current trend in telepresence research is to bring experts and facilities together from geographically dispersed locations (Hadida-Hassan et al., J. Struct. Biol., 125:229-234 [1999]; Parvin et al., “Visual Servoing for On-Line Facilities,” IEEE Computer Mag., pages 56-62 [1997]; Potter et al., UltraMicros., 77:153-161 [1999]; Young et al., J. Supercomputer Appl. High Perf. Comput, pp. 170-181 [1996]; and Parvin et al., “A Collaborative Framework for Distributed Microscopy,” IEEE Conf. on SuperComputing [1998]). This system, in addition to collaborative frameworks (Parvin et al., [1997], supra) is commonly used in the field. Telepresence research has focused on remote functionality of the instrument and necessary automation for large-scale data collection and analysis. The MASH project of the University of California, at Berkeley (McCanne, IEEE Internet Comput., 3:33-44 [1999]) uses MBone tools in a heterogeneous environment to develop scalable multi-media architecture for collaborative applications in fully distributed systems. NCSA's Habenero project provides smooth management and simultaneous distribution of shared information to all clients in a component-based centralized system that is primarily written in Java. Rutger University's DISCIPLE uses a CORBA framework for distributed access in a service-based, centralized system for enforcing shared virtual space. Sun Microsystem's Java Shared Development tool kit provides collaborative-aware Java code to send data to participants within a communication session. It supports three types of transport protocols, namely TCP/IP socket, light-weight reliable multicast, and remote invocation methods. In this framework, all objects are manageable and collaboration occurs within a session that includes channel, token, blobs, and listener. The University of Michigan's Upper Atmosphere Research Collaboratory (UARC) is a web-based distributed system that is mostly written in Java. This system collects data from over 40 observational platforms for space physics research for both synchronous and asynchronous collaboration. In this system, data suppliers publish their data on a data-dissemination server. Clients then subscribe, in order to receive the desired information.
In addition to the physical sciences, telepresence methods have been used in the biological sciences. For example, in the post-genomic sequencing era, quantitative imaging of complex biological materials is a critical problem. Indeed, sequential measurements obtained with different microscopy techniques preclude detailed analysis of multidimensional responses (e.g., in time and space). Quantification of spatial and temporal concurrent behavior of multiple markers in large populations of multicellular aggregates is hampered by labor intensive methods, a lack of quantitative tools, and the inability to index information. Ideally, methods would track the kinetics and quantities of multiple target proteins, their cellular context and morphological features in three-dimensions using large populations.
For example, there are several thousand antibodies and other reagents available for differentiating specific protein components of cells. Some antibodies can additionally discriminate between functional variants of proteins caused by modifications such as phosphorylation status, protein conformation, and complex formation. Of the intracellular proteins, a large number are involved in signalling pathways. These pathways are currently not well understood, due to the complexity of the potential events, the potential for multiple modifications affecting protein function, and lack of information regarding where and when a protein is actively participating in signalling. Inherent biological variability and genomic instability are additional factors that support the requirement for large population analysis. Thus, there is a need for microscopy and image analysis methods that are useful for developing a more detailed picture of cellular signalling. This is particularly true in the development of methods to diagnose and treat disease.
Today, various diseases are now understood at the molecular and genetic level. Analysis of molecules associated with disease is important for disease diagnosis and prognosis. However, the study of diseases such as cancer is currently limited by the techniques and model systems available for their characterization. Studies for the qualitative or quantitative analysis of protein and/or nucleic acid expression are compromised by the diverse cell populations in tissue samples which typically include a number of cell types (e.g., abnormal cells, epithelial cells, stromal cells, endothelial cells, inflammatory cells, etc.). Since the cells of interest (e.g., tumor cells) are often a relatively small percentage of the total cell population, it is difficult to interpret the significance of net protein or nucleic acid alterations in the typical specimen. In addition, studies of cells in culture do not account for the complex interactions that occur between cells. Furthermore, commonly used techniques rely on methods such as tissue fixation, antigen-antibody recognition, and/or histological stains that typically require that the cells analyzed be killed during the processing of the samples. This limits the amount of information available regarding the cells in the specimen.
The use of tools such as fluorescent probes and confocal microscopy have enabled the resolution of three dimensional intracellular spatial distribution of various molecular species and subcellular structures, as research in cellular physiology require the formation of theoretical hypotheses regarding experimentally observed phenomena. These hypotheses are then often formalized into mathematical models. These models are then incorporated in simulations, in an attempt to correlate experimental results with phenomena observed in vivo.
However, there are deficiencies in the existing technologies. In general, these deficiencies originate from the current limitations regarding the representation of cells. In these methods, cells are represented as ideal and simple geometric shapes with spatially homogenous behavior (i.e., physiology) and structure (i.e., anatomy). This prevents the researcher from actually expressing an observed physiological phenomena in a simulation that easily correlates to an actual experiment using an intact cell. Thus, the validation of the model and hypothesis is often very difficult.
Most current efforts in the modeling and simulation of cellular physiology are directed toward either very specific models of individual mechanisms or abstract representations of more complex phenomena. The specific models include models of individual molecular interactions (e.g., involving ion channels). The abstract models typically apply simplifications of the underlying mechanisms that are usually only appropriate to explain a limited class of physiological problems and/or observations. Thus, methods and devices are needed that facilitate real-time observations, correlations of observed phenomena with disease conditions, and means to observe cells in situ over time.
In addition, for some types of cancer (e.g., certain leukemias and testicular cancer), chemotherapy is successful in providing a cure to affected patients. However, in solid tumors (e.g., breast cancer), little progress has been made in improving therapy. Inherent or acquired multi-drug resistance (MDR) in solid tumors represents one important obstacle in providing cures via chemotherapy. In vitro chemosensitivity assays to assess drug response and predict patient response have been in development for over 40 years, but a truly successful in vitro chemosensitivity test has not been developed. Thus, there remains a need for reliable and meaningful in vitro chemosensitivity tests.